Smart coordinate conversion and calibration system in semiconductor wafer manufacturing

ABSTRACT

A smart conversion and calibration of the defect coordinate, diagnosis, sampling system and the method thereof for manufacturing fab is provided. The intelligent defect diagnosis method includes receiving pluralities of defect data, design layout data, analyzing the defect data, design layouts, by a Critical Area Analysis (CAA) system. This method utilizes the precisely calibrated coordinate, the defect layout pattern, and the higher accurate calibrated defect size value. So, a more precise killer defect index can be generated with calibrated coordinate deviation calibration and defect size deviation calibration. When judging a defect relating to short circuit or open circuit failure probability, the defect failure result is more accurate and less incorrect judgment.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. patentapplication Ser. No. 16/134,114 filed on Sep. 18, 2018, which claims thebenefit of U.S. Patent Application No. 62/559,784 filed on Sep. 18,2017, the content of which are hereby incorporated by reference in theirentirety.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present invention involves a smart semiconductor defect calibration,classification, and sampling system and the method thereof. Especiallyit is a smart defect calibration, classification, and sampling systemand the method thereof applied to the semiconductor manufacturing fab,semiconductor assembly fab, flat panel display manufacturing fab, solarpanel manufacturing fab, printed circuit board manufacturing fab, maskmanufacturing fab, LED fab or assembly fab.

2. Description of the Prior Art

In general, the manufacturing of integrated circuit wafer in asemiconductor fab includes processing with mask, lithography process,etching (plasma etch and wet etch), film deposition, Cu dual damasceneprocess, chemical mechanical polishing, and multi-patterning exposureprocesses and equipment. During the manufacturing stage, the randomdefect and systematic defect are normally created due to such factors asthe equipment's precision deviation itself, abnormal failure, particlecreated in the process, insufficient process window in the design layoutand lithography process matrix window check. Those random defect andsystematic defect will cause product's open or short failure. Reduce thewafer yield. Following semiconductor technology geometry shrinkage, therandom defect and systematic defect will increase in large quantity withshrinkage geometry dimension. Each time, there are several thousand, orseveral ten thousand defects detected in defect scan and inspection.Because the defect processing throughput is limited by the defect imagesthroughput taken from Scanning Electron Microscope. Fab team can onlyselect a small number of defect images (for example: 50 to 200 defectimages) for each defect inspection test. That makes defect sampleselection of real open circuit failure defect or short circuit failuredefect extremely difficult. Normally, real open circuit failure defector short circuit failure defect SEM images cannot be found in time toprocess engineer or the SEM images are useless without failure. So,process engineer can hardly judge where the root cause of wafer yieldfailure is based on the SEM images. The defect yield improvementefficiency is poor. This will increase semiconductor fab cost.

In the past, the real-time data mining of defect and image patternclassification data is an important method adopted to increase yield inthe operation of a semiconductor fab (for example: Foundry fab). But itbecomes very hard to identify failure killer defect for the previousmethod in the nanometer semiconductor fab process. The core part of thisinvention introduces integrated circuit (IC) design layout data,Critical Area Analysis (CAA) method, defect pattern overlapping designlayout pattern, defect to design layout coordinate conversioncalibration system, and defect size calibration system. Those method andsystem are important breakthrough to solve the killer defect samplingissue.

Furthermore, there is defect size deviation issue between the defectcontour's metrology data measured on the defect image pattern which isgenerated from the SEM and optical microscope and defect data generatedfrom the defect inspection tool. The Critical Area Analysis is comparedwith both defect sizes as an input defect size. Because the defect size,area deviation exists between defect data generated from the defectinspection tool and the defect contour's metrology data measured on thedefect image pattern which is generated from the SEM and opticalmicroscope. That makes deviation on the Critical Area Analysis result.In order to resolve the Critical Area Analysis deviation, the defectsize deviation issue must be solved. For example: The defect sizemetrology unit of the defect inspection tool is obviously higher thanthe minimum size unit of design layout pattern. That will result in thedefect size deviation issue between the defect size of the defectinspection data and the real defect size data of the SEM image.

Besides, design layout pattern geometry size keeps on shrinking in moreadvanced complex semiconductor process. Especially, lithography processwindow is getting narrower when optical limitation effect is gettingworse. That becomes worse when the IC design layout polygon counts areincreased in multiple times and layout patterns are drawn in morecomplex mode. So, some defects are related to certain design layoutpatterns which are detected from systematic defect inspection. Thosedefects that will impact yield are so-called systematic defect. It willcause very low yield. But there are defect layout patterns which do notimpact IC design circuits, for example: monitor layout patterns. Thosedefects will not impact yield. They belong to false defect. Usually,those false defects' pattern and signal are obviously stronger thanother defects. So, those false defects normally occupy the majority ofdefect sample quantities up to 90% level. That blocks the opportunity tofind out the real open failure or short failure defect pattern.

In defect sampling part, the same inventor with U.S. Pat. No.8,312,401B2 granted in 2012 uses Critical Area Analysis method tocalculate the critical area over defect location's design layout patternarea based on the defect size and the coordinate deviation region area.A Killer Defect Index (KDI) is calculated to assess the defect's opencircuit failure or short circuit failure probability. This is the KillerDefect Index, i.e. a CAA value. However, it does not count on theresolution of defect inspection tool. Especially, the layout patterngeometry dimension is smaller than the resolution dimension of defectinspection tool. For example: The resolution unit is +−w for a defectinspection tool. For example: When w is equal to 0.05 μm, the reporteddefect size dimension will be in multiple time of 0.05 μm. If the layoutpattern geometry dimension is 0.02 μm, then the reported defect sizewould be larger than real defect size. This defect size deviation willresult in incorrect KDI value, i.e. higher KDI value than real KDIvalue.

Based on consideration of the above-mentioned multiple technologies, howto overcome the above constraints to raise and improve the manufacturingefficiency of semiconductor fab is the common goal of the engineers andexperts in the defect analysis field.

SUMMARY OF THE INVENTION

The main purpose of the invention is to use the integrated circuit (IC)design layout and Critical Area Analysis method. In terms of thedeviation value generated from the defect inspection tool, thisinvention provides input the coordinate deviation calibration value andthe calibration factor for the defect size that can correct thecoordinate deviation and defect size deviation of the defect data fromthe defect inspection tool. Combining the integrated circuit designlayout data, the system overlaps the plurality of defect pattern ontothe mapped plurality of defect layout pattern one by one. Then, thesystem analyzes with Critical Area Analysis method to create the KillerDefect Index (KDI). This invention utilizes the precisely calibratedcoordinate, the defect layout pattern, and the higher accuratecalibrated defect size value. So, a more precise killer defect index canbe generated with calibrated coordinate deviation calibration and defectsize deviation calibration. When judging a defect relating to shortcircuit or open circuit failure probability, the defect failure resultis more accurate and less incorrect judgment. This new invention willbecome an important tool that can identify a defect belonging to akiller defect category or a non-killer defect category.

According to the purpose mentioned above, this invention provides asmart defect calibration system in semiconductor wafer manufacturing.The system includes a storage apparatus, wafer manufacturing equipment,defect inspection tool, and data processing apparatus. The storageapparatus is used to store the IC design layout files. The IC designlayout file includes plurality of circuit pattern. Wafer manufacturingequipment processes the circuit pattern layout of the IC design layoutonto wafer. The wafer defect inspection tool scans and inspects thewafer to report defect inspection data. The data processing apparatusconverts the defect scan and inspection data into a defect text data andimage data and store onto the storage apparatus. The characteristics is:Data processing apparatus retrieves a calibration value, i.e. retrievesa calibration value from the storage apparatus. The calibration value isthe relative coordinate calibration statistical value of the coordinatedeviation area when applies to the conversion of defect image coordinateto the defect layout pattern coordinate.

Based on the purpose mentioned above, the invention is to provide asmart coordinate conversion and calibration system in semiconductorwafer manufacturing, including a storage apparatus, a wafermanufacturing set having a wafer manufacturing equipment, a wafer defectinspection tool and a data processing apparatus, the storage apparatusis used to store an integrated circuit design layout file including aplurality of circuit layout patterns, the wafer manufacturing setarranges the circuit layout patterns of the integrated circuit designlayout files onto wafer, the wafer defect inspection tool scans thewafer to retrieve a defect scan and inspection data, the data processingapparatus converts the defect scan and inspection data into a defecttext and image data file including a plurality of defect data on thewafer, and each of the defect data at least includes a defectcoordinate, a defect size, a defect area and a defect intensity value ofa defect contour, wherein the data processing apparatus adjustsdimension units of the defect size, a defect coordinate and the circuitlayout pattern to be the same dimension unit; the data processingapparatus converts the defect contour onto a correct defect coordinate(X₂, Y₂) of the circuit layout pattern for a real-time pattern match;the data processing apparatus executes a coordinate deviationcalibration, which includes: a user performs a manual pattern match tomeasure the coordinate deviation calibration value, by adjusting thedimension unit of the displayed defect layout pattern and the defectcontour to be the same, and manually marking a defect contour locationto a corresponding location in the circuit layout pattern as a newcoordinate location (X₂′, Y₂′); the data processing apparatus obtainsthe coordinate deviation calibration value (X₂′-X₂, Y₂′-Y₂), thecoordinate deviation calibration value includes an average coordinateprecision value and a coordinate precision standard deviation value forX-axis and Y-axis, the average coordinate precision value and thecoordinate precision standard deviation value are calculated via astatistical analysis; and the data processing apparatus introduces theaverage coordinate precision value and the coordinate precision standarddeviation value into the smart coordinate conversion and calibrationsystem in order to overlap or map a defect coordinate (X₁, Y₁) to thenew coordinate location (X₂′, Y₂′) based on the coordinate deviationcalibration value (X₂′-X₂, Y₂′-Y₂) after the coordinate deviationcalibration.

Based on the purpose mentioned above, the invention further provides asmart coordinate conversion and calibration system in semiconductorwafer manufacturing, including a storage apparatus, a wafermanufacturing set having a wafer manufacturing equipment, a wafer defectinspection tool and a data processing apparatus, the storage apparatusis used to store an integrated circuit design layout file including aplurality of circuit layout patterns, the wafer manufacturing setarranges the circuit layout patterns of the integrated circuit designlayout files onto wafer, the wafer defect inspection tool scans thewafer to retrieve a defect scan and inspection data, the data processingapparatus converts the defect scan and inspection data into a defecttext and image data file including a plurality of defect data on thewafer, and each of the defect data at least includes a defectcoordinate, a defect size, a defect area and a defect intensity value ofa defect contour, wherein the data processing apparatus adjustsdimension units of the defect size, a defect coordinate and the circuitlayout pattern to be the same dimension unit; the data processingapparatus converts the defect contour onto a correct defect coordinate(X₂, Y₂) of the circuit layout pattern for a real-time pattern match;the data processing apparatus executes a coordinate deviationcalibration, which includes: a user performs a pattern match using aGraphical User Interface to measure the coordinate deviation calibrationvalue, and to mark a defect contour location to a corresponding locationin the circuit layout pattern as a new coordinate location (X₂′, Y₂′);the data processing apparatus obtains the coordinate deviationcalibration value (X₂′-X₂, Y₂′-Y₂), the coordinate deviation calibrationvalue includes an average coordinate precision value and a coordinateprecision standard deviation value for X-axis and Y-axis, the averagecoordinate precision value and the coordinate precision standarddeviation value are calculated via a statistical analysis; and the dataprocessing apparatus introduces the average coordinate precision valueand the coordinate precision standard deviation value into the smartcoordinate conversion and calibration system in order to overlap or mapa defect coordinate (X₁, Y₁) to the new coordinate location (X₂′, Y₂′)based on the coordinate deviation calibration value (X₂′-X₂, Y₂′-Y₂)after the coordinate deviation calibration.

Based on the purpose mentioned above, the invention further provides asmart coordinate conversion and calibration system in semiconductorwafer manufacturing, including a storage apparatus, a wafermanufacturing set having a wafer manufacturing equipment, a wafer defectinspection tool and a data processing apparatus, the storage apparatusis used to store an integrated circuit design layout file including aplurality of circuit layout patterns, the wafer manufacturing setarranges the circuit layout patterns of the integrated circuit designlayout files onto wafer, the wafer defect inspection tool scans thewafer to retrieve a defect scan and inspection data, the data processingapparatus converts the defect scan and inspection data into a defecttext and image data file including a plurality of defect data on thewafer, and each of the defect data at least includes a defectcoordinate, a defect size, a defect area and a defect intensity value ofa defect contour, wherein the data processing apparatus adjustsdimension units of the defect size, a defect coordinate and the circuitlayout pattern to be the same dimension unit; the data processingapparatus converts the defect contour onto a correct defect coordinate(X₂, Y₂) of the circuit layout pattern for a real-time pattern match;the data processing apparatus executes a coordinate deviationcalibration, which includes: the data processing apparatus performing anauto pattern match between the defect contour in the circuit layoutpattern and the defect contour in the defect text and image data file tomeasure the coordinate deviation calibration value, and to mark a defectcontour location to a corresponding location in the circuit layoutpattern as a new coordinate location (X₂′, Y₂′); the data processingapparatus obtains the coordinate deviation calibration value (X₂′-X₂,Y₂′-Y₂), the coordinate deviation calibration value includes an averagecoordinate precision value and a coordinate precision standard deviationvalue for X-axis and Y-axis, the average coordinate precision value andthe coordinate precision standard deviation value are calculated via astatistical analysis; and the data processing apparatus introduces theaverage coordinate precision value and the coordinate precision standarddeviation value into the smart coordinate conversion and calibrationsystem in order to overlap or map a defect coordinate (X₁, Y₁) to thenew coordinate location (X₂′, Y₂′) based on the coordinate deviationcalibration value (X₂′-X₂, Y₂′-Y₂) after the coordinate deviationcalibration.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is the operation architecture drawing chart of the smartsemiconductor defect calibration, classification, and sampling systemand the method thereof of the present invention.

FIG. 2 is the process flowchart of the smart system of the presentinvention.

FIG. 3A is the retrieve design layout drawing chart for the smart systemof the present invention.

FIG. 3B is the retrieve defect data drawing chart for the smart systemof the present invention.

FIG. 3C is the defect to design layout coordinate conversion drawingchart for the smart system of the present invention.

FIG. 4 is the defect to design layout coordinate conversion andcoordinate deviation calibration flowchart for the smart system of thepresent invention.

FIG. 5 is the defect to design layout coordinate conversion flowchartwith defect size calibration for the smart system of the presentinvention.

FIG. 6A to FIG. 6D are the multiple practice examples for coordinatedeviation calibration drawing chart for the smart system of the presentinvention.

FIG. 7A to FIG. 7E are the multiple practice examples for clippingdefect contour and overlapping to mapped design layout pattern drawingchart for the smart system of the present invention.

FIG. 8A and FIG. 8B are the setup defect size, defect area calibrationsystem flowchart for the smart system of the present invention.

FIG. 8C is the conversion table from the original defect size to finaldefect size through high resolution defect image calibration for thesmart system of the present invention.

FIG. 9 is the execute pattern match between defect contour and polygonpattern flowchart for the smart system of the present invention.

FIG. 10 is the defect classification flowchart for the smart system ofthe present invention.

FIG. 11A to FIG. 11G are the defect pattern library for the smart systemof the present invention.

DETAILED DESCRIPTION OF THE INVENTION

In semiconductor wafer fab, semiconductor assembly fab, flat paneldisplay fab, solar panel fab, printed circuit board fab, mask fab, LEDfab or LED assembly fab, there must be run through mask, lithography,etching, and film deposition, etc. equipment and processing method toform a specific functioning product. Because there are many complexprocess steps in the manufacturing procedures, the process and equipmentparameter control, equipment parameter shift, or technical bottleneckcan produce the defects that would impact yield. Generation of thosedefects is inevitable. So in the manufacturing procedures of asemiconductor fab, fab team would always execute defect inspection andanalysis to improve yield and reduce cost.

First of all, please review FIG. 1 . It is an operation architecturechart for the present invention that involves a smart semiconductordefect calibration, classification, and sampling system and the methodthereof. As shown in FIG. 1 , a practice example of the presentinvention will take the wafer manufacturing as an example and explain aswell. For the description procedure below, we will use smart system toreplace smart defect calibration system and the method thereof. Overall,the smart system can be implemented at wafer foundry fab 20 (a simplenaming as fab 20). The smart system can be implemented at IC designhouse 30 (a simple naming as design house 30). Of course, the smartsystem can be implemented through fab 20 and design house 30 via wiredinternet or through antenna 24/34 and wireless internet to implement.

For example: When a design house 30 completes a specific functioning ICdesign layout, data processing center 31 will store the design layoutpattern GDS or OASIS file into the memory unit 33. Then, the system willdeliver the GDS or OASIS file through wired internet or wirelessinternet to fab 20. The design layout pattern 1110 (shown in FIG. 3A)include plurality of layout pattern (i.e. device layout pattern). Eachlayout pattern polygon always includes layout pattern geometry size,layout pattern coordinate, layout pattern layer, text mark or dimension.In general, design layout pattern 1110 format can be GDS (GraphicDatabase System) format, GDS-II format, or OASIS (Open Access Same-timeInformation System) format. Fab 20 retrieve the data file and pass datafile to data processing center 21. Once data processing center 21processes the data file, the data will be saved into the memory unit 23.The mask is made based on the design layout file and OPC (OpticalProximity Correction) in mask house. After that, fab 20 will runsemiconductor process using various layer's mask. Then, plurality ofrepeating die 11 (shown in FIG. 3D) are made in wafer 10. Usually, fab20 will use the design house 30 design layout file to manufacturesemiconductor chips.

During the manufacturing procedures of wafer 10, defect will occur ineach manufacturing process of wafer 10. For example, random defect orsystematic defect. That is why fab 20 will use defect inspection tool todetect defect at any stage or multiple process steps in themanufacturing process. For example: Scanning Electron Microscope (SEM),E-beam inspection tool, optical inspection tool, defect scanner, orcamera, etc. The defect inspection tool will run defect scan andinspection on wafer 10 and generate the wafer's raw defect inspectiondata file. The defect inspection file includes defect size dimension,shape, area, die index, coordinate, or image, etc. The defect data willbe processed in the Data Processing Center 21 to be JPG, TIFF, PNG, andpure text spec defect text and image data file 1130 (shown in FIG. 3B).Then, save into the memory unit 23.

Based on the above information, fab 20 obviously has saved the designlayout file 1110 of a design house 30, defect text and image data file1130 into memory unit 23. So, Smart System of present invention canexecute defect calibration, classification, and sampling job at fab 20.Similarly, Smart System of present invention can execute defectcalibration, classification, and sampling job at design house 30 if fab20 delivers defect text and image file 1130 to design house 30 throughwired network or wireless network. Of course, Smart System of presentinvention can execute defect calibration, classification, and samplingjob as a real-time analysis for both fab 20 and design house 30 if bothfab 20 and design house 30 can exchange data files to each otherreal-time through wired network or wireless network. As to who is incharge to execute defect calibration, classification, and sampling job,the present invention does not limit to either side.

Next, please refer to FIG. 2 . It is the flowchart of the smart systemof the present invention. As shown in FIG. 2 , flowchart 100 of smartsystem of the present invention begins with data processing center 21.Data processing center 21 retrieves design layout pattern file 1110 anddefect text and image data file 1130 as shown in step 110 and step 120.Then, step 130 performs coordinate conversion and coordinate deviationcalibration with the design layout pattern file 1110 and defect text andimage data file 1130. The defect coordinate location of wafer 10 isconverted to the corresponding coordinate location in design layoutpattern. It is to decide where the defect image 1101 will fall on thedesign layout pattern coordinate location. After that, step 140processes each defect contour of the defect image to be superposed ormapped to design layout pattern 1110 where the superposition or mappinglocation is the mapping defect layout pattern 1111 coordinate locationwith coordinate conversion from each defect image 1101 coordinatelocation. Then, step 150 generates Killer Defect Index (KDI). KillerDefect Index is generated based on step 140 the defect patterncoordinate conversion of each defect image's contour to be superposed ormapped in design layout coordinate location. The Critical Area Analysismethod is used to analyze and produce the short and open critical areabased on the defect contour dimension and the layout pattern with acoordinate deviation area at the design layout coordinate location. Atsame time, step 160 can check whether there is overlap of defect imagecontour with two layout patterns. Step 160 can also check whether thereis intersect of defect image contour with one layout pattern. Then, step170 performs defect classification based on the judgment results of step150 and step 160. According to the KDI, defect signal parameter, patternmatch between defect and defect pattern library, and pattern matchbetween defect and frequent failure defect library, defectclassification is performed. The defect pattern library and frequentfailure defect library can be obtained from memory unit 23/33 (as shownin step 180), or process defect classification based on the layoutpattern interaction with defect, i.e. the short or open failure result.Finally, step 190 performs defect sampling based on the defectclassification and defect sampling rule setup in step 170.

Further, real practice way of each step in flowchart 100 of the smartsystem is described in detail. First of all, retrieval of design layoutpattern in step 110 is mainly processed based on the IC design layoutpattern 1110 of a design house 30. Please refer to FIG. 3A. It is achart about obtaining a design layout pattern in smart system of presentinvention. As shown in FIG. 3A, user (i.e. engineer in design house 30)retrieves design layout pattern 1110 in advance through the dataprocessing center 31. The design layout pattern 1110 format can be GDSformat, GDS-II format, or OASIS format.

Then, please see FIG. 3B. It is a chart about obtaining a defect datafile in smart system of present invention. As described before, a designlayout pattern file 1110 is already completed in design house 30 anddelivered to fab 20. Through manufacturing process, fab 20 can produceand form full chip layout 11 pattern on wafer 10. In full chip layout11, includes a plurality of dies. For example, 11D1, 11D2, 11D3. Then,fab 20 use defect inspection tool to do defect scan and inspection onwafer 10. So, obtain the plurality of defect images 1101 and defect textdata over the full chip layout 11. Then, data processing center 21extracts and calculates one or more defect images 1101 generated onwhich die of wafer 10 and the defect location on that die.

Furthermore, please see FIG. 3B. Data processing center 21 obtains everydefect coordinate (X₁, Y₁) of wafer 10, and obtains defect image 1101 inimage file 1001, and the circuit pattern 1102 in the image file 1001.Please continue on FIG. 3B. Out of many dies in wafer 10, there aretotal 7 defect images 1101 detected in die 11. Data processing center 21displays defect image coordinate (X₁, Y₁) with reference to the firstreference original coordinate (X₀₁, Y₀₁). For example, the firstreference original coordinate (X₀₁, Y₀₁) is generated from the defectinspection tool's recipe. Normally, first reference original coordinate(X₀₁, Y₀₁) is selected at die 11 corner or a very easy recognizedlocation as a reference marker. As to the marker, the present inventiondoes not put a limitation on it. Finally, data processing center 21obtains each defect text and image data file 1130. Then, use wafer 10link to the defect text and image data file 1130. In defect text andimage data file 1130, record each defect image's content, including die11 die index number, defect image 1101 identification number, productname, defect inspection step, lot ID, defect inspection tool ID, defectcoordinate (X₁, Y₁), and rough defect image dimension (including themaximum size in X direction and Y direction), etc. Finally, defect textand image data file 1130 will be stored in memory unit 23.

Then, perform defect data coordinate conversion and coordinate deviationcalibration in step 130. Please refer to FIG. 3C, it is a drawing chartin smart system of present invention about the coordinate conversionbetween design layout pattern and the defect data. As shown in FIG. 3C,data processing center 21 extracts every defect image 1101 and itscoordinate (X₁, Y₁) in die 11 from the defect text and image data file1130. The data processing center 21 processes the defect to designlayout pattern coordinate conversion. For example: Data processingcenter 21 handles the defect coordinate (X₁, Y₁) in defect text andimage data file 1130 through coordinate conversion process and maps tothe coordinate (X₂, Y₂) in defect layout pattern 1111 of a design layoutpattern 1110. The defect coordinate (X₁, Y₁) in defect text and imagedata file 1130 is converted to design layout coordinate (X₂, Y₂) basedon the design layout pattern 1110 reference marker coordinate. As shownin FIG. 3C, the 7 defect images 1101 of those defect ID 1˜7 is convertedto defect layout pattern 1111 with corresponding defect coordinate (X₂₁,Y₂₂) to (X₂₇, Y₂₇). As to the dimension size in defect layout pattern1111, it is determined by the defect inspection tool's precision levelor deviation range. For example: When use an optical defect inspectiontool with a deviation range −0.5 μm˜+0.5 μm to perform the defect scanand inspection, then the deviation range of the defect layout pattern1111 is 1 μm×1 μm. In the defect layout pattern 1111, the conductorlayout pattern dimension can be 50 nm and the spacing between twoconductor layout patterns can be 30 nm.

Besides, the purpose to convert the defect coordinate (X₁, Y₁) to designlayout coordinate (X₂, Y₂) in design layout pattern 1110 is to judgewhether a defect image 1101 would cause a circuit layout pattern 1102 tobe a short circuit defect or open circuit defect. However, defectcoordinate (X₁, Y₁) for defect image 1101 is measured from the defectinspection tool. The reference marker coordinate could be a die 11 andits first reference original point coordinate (X₀₁, Y₀₁) as a centerpoint. Design layout pattern 1110 does have its own reference originalpoint coordinate (X₀₂, Y₀₂). Since mask contains both the design layoutreference original point coordinate (X₀₂, Y₀₂) and reference markerlayout pattern coordinate, the relative position of design layoutreference original point coordinate to the reference marker layoutpattern coordinate is decided. When user select the reference markerlayout pattern coordinate (X₀₁, Y₀₁) for the defect inspection tool,then relative position of design layout reference original pointcoordinate to the reference marker layout pattern coordinate (X₀₁, Y₀₁)is calculated into the defect coordinate conversion system. In addition,there is coordinate deviation due to optical diffraction effect wheninspect wafer 10 in a defect inspection tool. In a drawn design layoutpattern, it is a rectangular pattern for the reference marker layoutpattern. However, this reference marker layout pattern becomes roundedpattern on wafer because the optical diffraction makes the originaldrawn rectangular marker pattern to be a curvature rounded pattern. So,there is a deviation between the defect image 1101 coordinate (X₁, Y₁)and the original drawn rectangular marker pattern. Obviously for thesame reference marker layout pattern, the deviation exists betweenreference marker pattern (X₀₁, Y₀₁) in wafer 10 and design layoutreference marker layout pattern. After defect coordinate conversion,this coordinate deviation is shown on the defect layout patterncoordinate. It will be corrected through the coordinate deviationcalibration system.

Furthermore, the file format of defect image 1101 is different with thefile format of design layout pattern 1110 in certain case. For example:When file format of defect image 1101 is JPEG, the unit is in pixel,micrometer or nanometer. When the file format of design layout pattern1110 is in GDS, the unit is in micrometer (μm), or nanometer (nm). Theremight exist deviation between those different file format. So in abetter practice example of the present invention, a precisioncalibration procedure is added and is shown in step 200. The actualcalibration procedure of step 200 is shown in FIG. 4 . FIG. 4 is thecalibration process flowchart of the present invention for defect todesign layout pattern coordinate conversion and deviation calibration.In the beginning, retrieve the design layout pattern file and defectdata file as shown in FIG. 4 step 110 and step 120. Since the proceduresare same as in FIG. 2 . There is no need to repeat description here.Next, please see step 210. Step 210 is to adjust both units in defectimage 1101 and design layout pattern 1110 to be the same unit. Forexample: User can choose a unit in pixel, μm, or nm, then adjust bothunits in defect image 1101 and design layout pattern 1110 to be the sameunit. After that, step 220 can be finished. The defect image 1101 can becorrectly transformed to design layout pattern 1110. In this way, thesystem can overcome the large defect to design layout coordinateconversion deviation problem that is induced from the difference betweenfile format of defect image 1101 and file format of design layoutpattern 1110.

In order to make the best precision for the defect image to designlayout pattern coordinate conversion and deviation calibration, all thepossible factors that might affect the coordinate conversion precisionare included for calibration. More than that, the present inventionprovides a better practice example. Please refer to FIG. 5 . It is aflowchart for defect size calibration and coordinate conversion. Asshown in FIG. 5 , the first step 2110 is to retrieve the parameters fromdefect inspection tool. For example: Data processing center 21 retrievesfrom memory unit 23, the alignment reference marker coordinate,dimension data, etc. of a defect inspection tool. Or step 2120retrieving the parameters of a design layout pattern 1110. For example:Data processing center 21 retrieves from memory unit 23 the originalcoordinate, alignment reference coordinate, and dimension data, etc. ofa design layout pattern 1110. Or step 2130 retrieving the parameters ofa mask. For example: Data processing center 21 retrieves from memoryunit 23 the original coordinate, alignment reference coordinate, centerpoint coordinate, and dimension data, etc. of a mask. Then, adjust thedimension of defect image 1101, dimension of design layout pattern 1110,and proportional dimension as defined in mask data to be the same asshown in step 2140. Without step 2140, step 220 cannot be completed.User must choose one or multiple reference marker pattern to setup thealignment mark reference marker coordinate for the defect inspectiontool. The marker pattern can be a L shape pattern, a cross pattern (+),or a rectangular pattern, etc. The marker pattern is a simple patternthat can be aligned easily. In general case, the marker pattern isplaced in the scribe line that is close to the die corner. That meansthe marker pattern is not placed inside design layout. Only mask dataincludes all the marker pattern coordinate inside scribe line, designlayout corner and original point coordinate, and mask center pointcoordinate. That is why all the distances between markerpattern/alignment marker and design layout pattern 1110 original pointcan be calculated based on the mask parameters data. The coordinateconversion system which converts the defect image coordinate to designlayout pattern 1110 coordinate can be setup through the above markerpattern/alignment marker and design layout pattern 1110 original pointcoordinate relationship. So, a defect image 1101 of an image file 1001coordinate (X₁, Y₁) can be converted to design layout pattern 1110coordinate (X₂, Y₂) correctly. In the end, processing step 220 in thispractice example will guarantee the coordinate deviation calibration, nomatter in the coordinate conversion deviation calibration or real timepattern match between defect image 1101 of image file 1001 and designlayout pattern 1110, with the coordinate deviation data as shown in step230.

Please refer to FIG. 4 . When step 220 already implements coordinatedeviation calibration for all possible coordinate deviation factors, thedefect image 1101 of image file 1001 is converted to a defect layoutpattern 1111 coordinate (X₂, Y₂) in design layout pattern 1110.Obviously, each defect layout pattern 1111 represents different layoutpattern and different image pattern 1101. For example: Wafer 10 containsa thousand die 11D. There might be coordinate deviation in each defectlayout pattern when the defect image 1101 defect coordinate (X₁, Y₁) isconverted to design layout pattern 1110 defect coordinate (X₂, Y₂). So,the present invention further provides three methods to calibrate designlayout pattern 1110 defect coordinate (X₂, Y₂) deviation. First method.As shown in step 2410, data processing center 21 selects a defect image1101 from memory unit 23. For example: Select a design layout patternthat includes the transistor device. Next, data processing center 21retrieves a defect layout pattern 1111. Then, data processing center 21retrieves defect image 1101 of image file 1001 that has the transistordevice pattern. Display both the defect layout pattern 1111 and defectimage 1101 of image file 1001 together on the computer monitor screen51. In a practice example, the dimension unit is already adjusted to bethe same in both displayed defect layout pattern 1111 and defect image1101 of image file 1001. (For example: Both patterns are adjusted todimension unit such as pixel unit, μm unit, or nm unit.) Then, user whoexecute the calibration manually processes the coordinate deviationdistance from the defect layout pattern coordinate to actual defectlayout pattern coordinate with mapped defect image on the monitor screen51 for a certain defect image/defect layout pattern pairs and calculatethe statistical coordinate deviation data. For example: On monitorscreen 51, the coordinate deviation calibration sponsor aligns defectlayout pattern 1111 and defect image 1101 of image file 1001 with asetting coordinate value manually as shown in FIG. 6A upper half. If thedefect coordinate location (X₂, Y₂) in the converted defect layoutpattern 1111 is not in the same coordinate location with the newcoordinate location (X₂′, Y₂′) in the defect image 1101, then acoordinate deviation calibration must be performed to calibrate to newcoordinate location (X₂′, Y₂′). For example: Coordinate deviationcalibration sponsor manually marks the defect image 1001 location to thecorresponding location in defect layout pattern 1111 as a new coordinatelocation (X₂′, Y₂′). Obviously, the location of defect image file 1001is converted to defect layout pattern 1111 actual defect coordinatelocation with the coordinate deviation calibration value as (X₂′-X₂,Y₂′-Y₂) as shown in lower half of FIG. 6A. Collect a certain amount ofcoordinate deviation calibration data. For example: Collect at least 51coordinate deviation calibration data. As shown in step 250, dataprocessing center 21 will process the data in a table and performsstatistical analysis. Then, the system can generate an averagecoordinate precision value and coordinate precision standard deviationfor X-axis and Y-axis as the coordinate calibration factor as shown inFIG. 6D. In a better practice example: If data processing center 21 withenough memory capacity and fast processing speed, then user can chooseto do match for each defect layout pattern 1111 and each defect image1101 of image file 1001. For example: Match 10000 defect images 1101 andget an even more accurate statistical data as the coordinate deviationcalibration data or calibration factor data. The present invention doesnot put a limit on this. Finally, retrieve the accurate statistical dataas the coordinate deviation calibration data or calibration factor dataas shown in step 260. The average coordinate precision value andcoordinate precision standard deviation can be introduced into thecoordinate conversion system. For the defect image 1101 in the defectlayout pattern 1111 through coordinate conversion, a coordinatedeviation calibration is performed. The coordinate deviation value is(X₂′-X₂, Y₂′-Y₂) or average coordinate precision value and coordinateprecision standard deviation for X-axis and Y-axis after processingstatistical analysis of coordinate deviation value (X₂′-X₂, Y₂′-Y₂).Finally, the file 1150 after correcting the coordinate deviation isstored in the memory unit 23.

Besides, the present invention can select another calibration method toproduce the accurate coordinate deviation calibration data. As shown instep 2420, data processing center 21 retrieves the first transistordevice defect layout pattern 1111. Then, data processing center 21retrieves defect image 1101 of image file 1001 that has the transistordevice pattern. Display both the defect layout pattern 1111 and defectimage 1101 of image file 1001 together on the computer monitor screen51. In a practice example, the dimension unit is already adjusted to bethe same in both displayed defect layout pattern 1111 and defect image1101 of image file 1001. (For example: Both patterns are adjusted todimension unit such as pixel unit, μm unit, or nm unit.) Then,coordinate deviation calibration sponsor processes calibration through aGraphical User Interface (GUI). Coordinate deviation calibration sponsormoves computer mouse cursor on monitor screen 51 to new coordinatelocation (X₂′, Y₂′) based on the defect image 1101 location relative tocircuit layout pattern 1102 and the corresponding location in defectlayout pattern 1111 with same circuit layout pattern as shown in upperhalf of FIG. 6B. For example: Coordinate deviation calibration sponsormanually moves cursor to defect image 1101 location mapped to thecorresponding location in defect layout pattern 1111 and marks as a newcoordinate location (X₂′, Y₂′). Then, defect image 1101 defectcoordinate (X₁, Y₁) is converted to design layout pattern 1110 defectcoordinate (X₂, Y₂). If defect image 1101 in the defect layout pattern1111 on converted defect coordinate location (X₂, Y₂) is not in the samecoordinate location with the new coordinate location (X₂′, Y₂′) in thedefect image 1101 and the defect layout pattern 1111, then coordinatedeviation calibration value can be obtained through clicking mouse tothe new coordinate location (X₂′, Y₂′) in GUI. The coordinate deviationcalibration data is (X₂′-X₂, Y₂′-Y₂) as shown in lower half of FIG. 6B.After that, follow the procedures from step 250 to step 260 and performsa certain amount of coordinate deviation calibration as same as in FIG.6A. The system can generate an average coordinate precision value andcoordinate precision standard deviation for X-axis and Y-axis as thecoordinate calibration factor. The average coordinate precision valueand coordinate precision standard deviation can be introduced into thecoordinate conversion system. It is same as described in above item.

In addition, the present invention can select another calibration methodto create accurate coordinate deviation calibration data. As shown instep 2430, data processing center 21 retrieves the first transistordevice defect layout pattern 1111. Then, data processing center 21retrieves defect image 1101 of image file 1001 that has the transistordevice pattern. Display both the defect layout pattern 1111 and defectimage 1101 of image file 1001 together on the computer monitor screen51. In a practice example, the dimension unit is already adjusted to bethe same in both displayed defect layout pattern 1111 and defect image1101 of image file 1001. (For example: Both patterns are adjusted todimension unit such as pixel unit, μm unit, or nm unit.) Then, dataprocessing center 21 performs auto pattern match between circuit layoutpattern 1113 in defect layout pattern 1111 and circuit pattern 1102 indefect image 1101 of image file 1001 as shown in middle figure of FIG.6C. Coordinate deviation calibration sponsor can mark new coordinatelocation (X₂′, Y₂′) where location (X₁, Y₁) of the defect image 1101 ismapped to the corresponding location in defect layout pattern 1111.Since defect coordinate (X₁, Y₁) of defect image 1101 is converted to adefect coordinate (X₂, Y₂) of design layout pattern 1101. If defectimage 1101 in the defect layout pattern 1111 on converted defectcoordinate location (X₂, Y₂) is not in the same coordinate location withthe new coordinate location (X₂′, Y₂′) in the defect image 1101 and thedefect layout pattern 1111, then coordinate deviation calibration valuecan be obtained through system coordinate deviation calibration. Thecoordinate deviation calibration data is (X₂′-X₂, Y₂′-Y₂) as shown inFIG. 6C lower half.

After that, follow the procedures from step 250 to step 270 and performsa certain amount of coordinate deviation calibration as same as in FIG.6A. The system can generate an average coordinate precision value andcoordinate precision standard deviation for X-axis and Y-axis as thecoordinate calibration factor. The average coordinate precision valueand coordinate precision standard deviation can be introduced into thecoordinate conversion system. It is same as described in above item.

The method described in FIGS. 6A, 6B, and 6C explains that the presentinvention can offer multiple practice methods to provide accuratecoordinate deviation calibration data. No matter which method is chosen,i.e. from any method in FIG. 6A, 6B, or 6C, the system can generatecoordinate deviation calibration data or an accurate statistical datafor coordinate deviation calibration or deviation calibration factor byprocessing step 250 to step 260.

After completion of step 200, the smart system of the present inventionalready obtains the defect image 1101 to design layout pattern 1110coordinate conversion with coordinate deviation calibration. Thecoordinate deviation calibration data is (X₂′-X₂, Y₂′-Y₂) or statisticalanalysis value of coordinate deviation calibration data (that is theabove-mentioned coordinate calibration factor). For example: averagecoordinate precision value and coordinate precision standard deviationfor X-axis and Y-axis. Next, defect image 1101 is created in designlayout pattern 1110. It is used to judge whether this defect image 1101is an open circuit failure killer defect or a short circuit failurekiller defect. The defect image 1101 or its contour is image pattern.Design layout pattern 1110 is GDS or OASIS format. Since there is nodefect image in design layout pattern 1110, it is impossible to executeshort circuit failure or open circuit failure analysis with the defectimage 1101. Since defect image 1101 contour pattern is a possibleirregular shape pattern. The present invention provides a clip defectcontour method for the defect image 1101 to obtain defect size dimensionand area of defect image 1101. It is used to be the foundation to judgea short circuit failure killer defect or open circuit failure killerdefect.

As shown in step 140 and FIG. 7A to FIG. 7D, it is the drawing chart ofthe present invention from clip defect contour of a defect image tooverlap a design layout pattern on the defect coordinate location.First, data processing center 21 of the smart system retrieves a defectimage 1101 contour dimension of an image file 1001 from the defect textand image data file 1130. The defect dimension data includes maximumdimension in X-axis and maximum dimension in Y-axis. Based on theclipped contour dimension of the defect image 1101, data processingcenter 21 creates a polygon defect image 1103 pattern which has theidentical X-axis and Y-axis dimension of a defect contour. For example:If the maximum X-axis dimension is 0.1 μm and the maximum Y-axisdimension is 0.08 μm, then the polygon defect image 1103 pattern area is0.008 μm² as shown in FIG. 7A lower arrow. Superpose or map the clippedcontour pattern of defect image 1101 or polygon defect pattern 1103 tothe defect layout pattern 1111 location (X₂′, Y₂′) under one of thecoordinate calibrations, i.e. coordinate after coordinate deviationcalibration, or coordinate deviation calibration value (X₂′-X₂, Y₂′-Y₂),or statistical value of coordinate deviation calibration data. Thesystem can judge whether dimension of the clipped contour pattern ofdefect image 1101 or polygon defect pattern 1103 will create killerdefect impact on the short circuit failure or open circuit failure. Asshown on FIG. 7B right hand side defect layout pattern 1111, it is akind of short circuit failure killer defect, i.e. two circuit layoutpatterns 1113 connected together by a defect image 1101. As shown onFIG. 7B left hand side defect layout pattern 1111, it is a kind of opencircuit failure killer defect, i.e. one broken circuit layout pattern1113 intercepted by a defect image 1101. Then, step 150 or step 160 canbe used to judge whether there is open circuit failure killer defect orshort circuit failure killer defect in defect image 1101 or defectlayout pattern 1111.

Next, perform Critical Area Analysis (CAA) method on step 150. When dataprocessing center 21 already overlaps the clipped polygon defect image1103 area onto the mapped coordinate of defect layout pattern 1111 ofdefect image 1101. Now, the system can utilize CAA method to analyzecritical area for the clipped polygon defect image 1103 area and themapped defect layout pattern 1111. Then, the system can judge shortcircuit failure or open circuit failure probability of a defect. Thisprobability value for a defect is a Killer Defect Index (KDI), i.e. aCAA value. For example: The system overlaps every clipped polygon defectimage 1103 onto the mapped defect layout pattern 1111. Then, systemjudges whether there is a short circuit failure or open circuit failureon the circuit layout pattern 1113. At same time, the system can judgethe Killer Defect Index value from the analyzed critical area valuebased on the polygon defect image 1103 and circuit layout pattern 1113.Please see killer defect judgment in FIG. 7C (i.e. defect ID 6 in FIG.3C). When dimension of the clipped defect image 1101 or the clippedpolygon defect image 1103 is much below the dimension of circuit layoutpattern 1113 width or spacing between two circuit layout pattern 1113,the defect will not cause either open circuit failure or short circuitfailure. For example: Dimension of a polygon defect image 1103 is 0.008μm. Dimension of a circuit layout pattern 1113 width is 0.1 μm.Dimension for the spacing between two circuit layout patterns 1113 is 0μm. No matter it is a defect image 1101 or a clipped polygon defectimage 1103, the defect will not cause either open circuit failure orshort circuit failure. The judged critical area is 0. So, Killer DefectIndex is 0, i.e. KDI=0. Dimension of a polygon defect image 1103 (Forexample: 0.11 μm) is equal or close to dimension of circuit layoutpattern 1113 width (For example: 0.1 μm). It is possible to cause shortcircuit failure or open circuit failure. Since the probability that adefect image 1101 pattern or polygon defect image 1103 will fall oncircuit layout pattern 1113 of the defect layout pattern 1111 is relatedto the circuit layout pattern 1113 area ratio of the defect layoutpattern 1111. As shown in FIG. 7C, when the critical area of circuitlayout occupies only 1/10 of the total coordinate deviation region areainside a defect layout pattern 1111, then the judged killer defect areais 0.1. So, Killer Defect Index is 0.1, i.e. KDI=0.1. That is to say.The short circuit failure or open circuit failure probability of thecircuit layout pattern 1113 in defect layout pattern 1111 (shown in FIG.7C) caused by a polygon defect image 1103 is 0.1.

FIG. 7D is used to describe the implementation of how to analyze andjudge Killer Defect Index. As shown in FIG. 7D, Critical Area Analysismethod used in the present invention is often used in design formanufacturing to simulate wafer yield. It analyzes the critical area ofan IC design layout with artificial defect data. The artificial defectdata is generated with Monte-Carlo method or similar method with randomdefect generation. It is not a real defect data output from a defectinspection data. Those randomly generated defect data are spreadrandomly over different coordinate location in a full chip designlayout. The purpose is to simulate the artificial defect that will causepossible yield loss and get the possible wafer yield with this kind ofdefect distribution assumption. CAA in design for manufacturing waferyield simulation is not a real wafer defect analysis. CAA used in thepresent invention is to analyze defect data from defect inspection tool.The clipped defect image 1101 and its defect size dimension, area isconverted to the corresponding coordinate in the defect layout pattern1111. Then, calculate the critical area based on the defect image 1101,defect size dimension, area, and the defect layout pattern 1111information within a coordinate deviation region area. (As describedbefore, defect can be in any coordinate location inside this coordinatedeviation region area because the deviation from the defect inspectiontool's stage movement motor precision error. The Killer defect Index(KDI) is equal to the critical area from CAA analysis divided by thetotal coordinate deviation region area. This KDI value represents theprobability of open circuit failure or short circuit failure for adefect and the defect's mapped defect layout pattern. As shown in leftside pattern of FIG. 7D, defect image 1101 or clipped polygon defectimage 1103 does not cause open circuit failure or short circuit failure.This defect is not a killer defect. The critical area is judged as 0.KDI is used for defect sample selection. When KDI is equal to 0 or veryclose to 0, that represents the open circuit failure or short circuitfailure probability of this defect is very low. As a result, this defectis not selected in defect sample selection. As shown in right sidepattern of FIG. 7D, the dimension of a defect image 1101 or a polygondefect image 1103 (For example: 0.11 μm) is equal or close to dimensionof circuit layout pattern 1113 width (For example: 0.1 μm). It ispossible to cause short circuit failure or open circuit failure. Theanalyzed open circuit failure critical area is named as Open CriticalArea (OCA). The analyzed short circuit failure critical area is named asShort Critical Area (SCA). It is shown as the area defined by dashedline in FIG. 7E. Because Open Critical Area and Short Critical Area willcause either systematic defect or random defect failure. So, OpenCritical Area and Short Critical Area must be added together (notcounting overlapped region twice). Then, this (OCA+SCA) is divided bytotal coordinate deviation region area. (As described, the coordinatedeviation after defect scan and inspection is −0.5 μm˜+0.5 μm for anoptical defect inspection tool. Total coordinate deviation region areais 1 μm×1 μm for the defect layout pattern 1111) For example: Theconductor width in a defect layout pattern 1111 is 50 nm. The spacingbetween two different conductors is 30 nm. Dimension of a defect image1101 pattern is 60 nm. Obviously, this 60 nm dimension of defect image1101 is a killer defect no matter what coordinate location it is insidethe defect layout pattern 1111. When Open Critical Area (OCA) is equalto 0.7 μm² and Short Critical Area (SCA) is equal to 0.3 μm². Assumethere is no overlap of OCA and SCA. The defect sample selectionindicator KDI is equal to 1 from calculation as described as 0.7 μm²+0.3μm²/1 μm>1 μm=1. When defect sample selection indication KDI is equal to1 or very close to 1, it represents the open circuit failure or shortcircuit failure probability of the defect is very high. This defect isselected as high failure probability. In the end, the defect sampleprobability of the plurality of polygon defect images 1103 will berecorded into memory unit 23.

Besides, the present invention can also choose step 160 to judge whethera defect will cause open circuit failure or short circuit failureprobability or not. Clip a defect image 1101 of image file 1001 (thisimage file 1001 is same as described at prior section. Include defectimage 1101 contour and its coordinate location relative to surroundingcircuit pattern) directly. Then, overlap this clipped defect image 1101onto the mapped defect layout pattern 1111 (as shown in middle of FIG.6C). After that, the system can judge whether this defect image 1101 isan open circuit failure killer defect or a short circuit failure killerdefect. For example: Data processing center 21 clips directly anoriginal defect image 1101 contour of image file 1001 and overlaps thisdefect image contour to the mapped (relative to defect image 1101)defect layout pattern 1111. Now, either data processing center orengineer can perform pattern match based on the original defect image1101 contour and defect layout pattern. Judge whether this originaldefect image contour is an open circuit failure type failure defect orshort circuit failure type failure defect. If the result is either anopen circuit failure or short circuit failure, then it is judged as akiller defect. The Killer Defect Index is 1. If the result is neither anopen circuit failure nor short circuit failure, then it is judged as anon-killer defect. The Killer Defect Index is 0. Last step, recordKiller Defect Index (KDI) results of those defect image 1101 into memoryunit 23. Since this practice example is to clip directly an originaldefect image 1101 contour of image file 1001 and overlap this defectimage contour to the mapped (relative to defect image 1101) defectlayout pattern 1111. Obviously, the system can judge open circuitfailure result, short circuit failure result, and decide KDI value ofdefect image 1101 directly. A better practice example in process step160 is shown as follows. First, process step in FIG. 4 or FIG. 5 . Thatmeans obtaining correct defect coordinate of original defect image 1101contour and converting accurately to the mapped defect layout pattern1111 coordinate (relative to defect image 1101 coordinate) are verycritical. Besides, another better practice example in process step 160is shown as follows. Defect image 1101 of image file 1101 is an imagefile generated from the Scanning Electron Microscope (SEM) defect scanresult. Because the high precision level from SEM defect scan, thecoordinate in the original defect image 1101 of a SEM image file is theactual defect location. So, KDI value of the defect image 1101 can beknown directly. That is why the KDI value is either 1 or 0 under thispractice example. The purpose to overlap this original defect image 1101contour to the mapped defect layout pattern 1111 (relative to defectimage 1101) is to know exactly what defect layout pattern 1111coordinate location the defect image 1101 falls on. So, design house canmodify design layout based on the defect layout pattern that wouldcreate failure killer defect.

Based on above description: When doing killer defect index (KDI)analysis or Critical Area Analysis (CAA) of defect image 1101, smartsystem of the present invention can choose original defect image 1101contour of image file 1001 and overlap this defect contour to the mappeddefect layout pattern 1111 of defect image 1101. It is shown in step150. About this, it is not limited by the present invention.

According to description before, fab team processes defect scan andinspection of wafer 10 to get defect image 1101. In order to achievehigh speed scanning purpose, fab normally selects microscope tool,E-beam inspection tool, optical inspection tool, defect scan tool, orcamera etc. to obtain defect inspection data (For example: defect size,width, coordinate, or contour, etc.) of the wafer. When using theoptical inspection tool to scan wafer and create defect image 1101,there is unclear defect image due to insufficient resolution abilityfrom the optical inspection tool. That is because the defect scanresolution is related to the optical inspection tool's lens andwavelength. If the pattern to be scanned on wafer 10 becomes smallerenough, then the resolution of the optical inspection tool cannotinspect and compare the pattern of the wafer 10 clearly. A vague defectimage 1101 is generated under insufficient resolution issue. Forexample, when defect image 1101 is a defocus image, the rough edge ofthe defect image 1101 is hard to be correctly judged in the clip ofdefect image 1101 contour. That will make the defect image 1101 fromdefect scan to be larger than its actual defect image. This incorrectdimension of defect image 1101 will generate incorrect killer defect andnon-killer defect classification due to this misjudgment. Besides, everyoptical inspection tool has its own resolution limit. If the resolutionis insufficient in the defect inspection of smaller geometry pattern,that means the minimum dimension unit used in defect scan and defectjudgment of an optical inspection tool is larger than minimum geometrydimension of the layout pattern. Under such circumstance, defect image1101 pattern is a defocus image. This will result in killer defectmisjudgment. For example: The minimum resolution unit is 50 nm in anoptical inspection tool. An actual X-axis defect size dimension andY-axis defect size dimension are 35 nm. So, the minimum dimension to bejudged by this optical inspection tool is 50 nm. The original inspectiondefect report in defect text and image data file 1130 is recorded in themultiplication of minimum dimension unit for the scanned and detecteddefect image 1101 dimension and area. This defect image 1101 sizedimension and area are much larger the actual defect image 1101dimension and area using a 1 or 2 nm resolution level SEM tool to obtainclear defect image 1101. Obviously, the incorrectness of original defectsize will impact Killer Defect Index analysis result. The non-killerdefect or negligible risk killer defect will be judged as a high-riskkiller defect incorrectly. For example: The actual defect image 1101size and area is not a short circuit failure or open circuit failuredefect. Due to minimum dimension unit of insufficient resolution and thedefocus defect image result, the area of plurality defect image 1101 inthe defect report file results in too many high-risk killer defects.That will lower the chance of selecting real open circuit failure defectsample or short circuit failure defect sample. This will result inslower yield improvement time and increase cost. Very clearly, thisdefect size of the defect image 1101 comes from the original defectinspection report file that is generated from the defect inspection toolwith insufficient minimum dimension resolution problem. So, theincorrect defect size must be calibrated to approach real defect sizedimension. Then the system can make correct defect classificationjudgment. So, the success rate of selecting open circuit failure defector short circuit failure defect can be raised.

In order to solve the resolution problem suffered from the wafer 10defect inspection result of optical inspection tool, the presentinvention provides a defect size calibration method to calibrate defectsize and area of defect image 1101. It is shown in FIG. 8A step 500.FIG. 8A is the step defect size and area calibration system processflowchart of the present invention. Precise defect size calibration isthe only way to Critical Area Analysis (CAA) and Killer Defect Index(KDI) precision calibration. Left side in FIG. 8B is data processingcenter 21 retrieves original defect size, area of defect inspectionreport from the defect text and image data file 1130. Then, dataprocessing center 21 clips defect contour of higher resolution defectimage file from the defect text and image data file 1130. In the betterpractice example, higher resolution defect image file means the defectsize and area from SEM image file. After that, use the higher resolutiondefect size and area result from SEM image file to calibrate theoriginal defect contour size and convert to polygon defect pattern. InFIG. 8C, the table shows that the original defect size on the left sideis converted to approximate to actual defect contour size through thehigher resolution defect image file calibration. Detail description isexplained as below.

As shown in FIG. 8A, it is a Critical Area Analysis and Killer DefectIndex Calibration flowchart 500 of the present invention. It begins withdata processing center 21 to retrieve defect text and image data file1130. First, it is shown in step 120. Data processing center 21retrieves original defect data (including text file and defect imagefile) from defect text and image data file 1130. Next, it is shown instep 510. Data processing center 21 retrieves original defect size inX-axis and Y-axis, and defect area of polygon defect image 1103 fromdefect text and image data file 1130. When the resolution of an opticalinspection tool is not good enough for the defect scan, it will generateincorrect and larger defect size than real defect size. For example:Resolution of an optical inspection tool is 50 nm. The minimum dimensionfrom defect inspection report is 50 nm. Even though the original defectsize is less than 50 nm, the defect report from an optical inspectiontool is in multiple times of 50 nm to display. So, there is a deviationbetween the minimum dimension detected in an optical inspection tool andthe minimum dimension detected in a better resolution SEM tool (Forexample: SEM resolution is 2 nm). For example: It is shown in FIG. 8C.The original defect size for 3rd defect image 1101 is 50 nm in X-axisand 50 nm in Y-axis. The original defect size for 4th defect image 1101is 150 nm in X-axis and 150 nm in Y-axis. The Killer Defect Index for3rd defect image 1101 is 0.4. The Killer Defect Index for 4th defectimage 1101 is 1. Then, it is shown in step 520. Data processing center21 selects high resolution defect image 1101 from memory unit 23. Dataprocessing center 21 clips defect image contour defect size and defectcontour area of defect image 1101. For example: Use the SEM defect imagewith 3 nm resolution. The system can analyze that the SEM defect sizefor 3rd defect image 1101 is 35 nm in X-axis and 35 nm in Y-axis. Thesystem can analyze that the SEM defect size for 4th defect image 1101 is100 nm in X-axis and 120 nm in Y-axis. After defect size calibration forthis practice example, more accurate KDI value can be obtained withprecision calibration. For example: It is shown in FIG. 8C. The realKiller Defect Index after defect size calibration for 3rd defect image1101 is 0.1. The real Killer Defect Index after defect size calibrationfor 4th defect image 1101 is 0.55. Next, it is shown in step 530. Applystatistical method to find the calibration factor between pluralityoriginal defect size, defect area data group, and plurality highresolution defect image contour size, defect contour area data group.Find the best calibration factor from the best statistical method. It isshown in step 540. Setup a defect size calibration system and metrologyuncertainty analysis with statistical method. Convert the originaldefect size data, which is generated from an in-line defect inspectionof an optical inspection tool, to approximate real defect size data. Asto the real defect size conversion procedure, it will be described innext section in detail. Besides, one important thing is why not usebetter resolution SEM tool to do defect scan and inspection directly.Instead, process defect inspection in a more complex calibrationprocedure. That is because the defect inspection is scanned afterfinishing wafer 10 process step. Although SEM tool resolution is goodenough, SEM tool operation is much more complex. SEM tool defectinspection throughput can only handle 1% of current original defect datavolume. In order to speed up process time, it is impossible to use SEMto scan all the wafers. Fab team has to use optical inspection tool.Without doing defect size calibration, it will result in incorrectKiller Defect Index judgment for optical inspection tool. This will notjust impact defect classification, but also impact wafer yieldimprovement. Increase fab cost. It is shown in step 550. Processingdefect size calibration is same as raising Killer Defect Index precisionlevel from Critical Area Analysis and judging killer defectclassification precisely. In this practice example, selecting number ofbetter resolution SEM images to do statistical analysis is not limitedby the present invention. Furthermore, fab team can choose SEM tool orother advanced defect inspection tool to do defect scan and inspectionin advanced technology fab process if SEM tool defect scan speed isimproved fast enough or other advanced defect inspection tool canprovide fast defect inspection. Under such circumstance, the accurateKDI value of a defect can be decided as described in step 160. Eventhough process shrink continues to small geometry to be equal or smallerthan SEM resolution, the above defect size calibration system and methodis still applied to any new defect inspection tool and camera tool. Forexample: Design layout pattern minimum geometry dimension is 1, 2 nm, orlower than 1 nm. Then, layout pattern minimum geometry dimension isequal to or smaller than SEM resolution. It is not limited by thepresent invention.

Please refer to FIG. 8C. Incorrect original defect size data isgenerated from fab in-line defect inspection tool. The defect sizecalibration process flow is to execute defect size calibration andmetrology uncertainty analysis through this defect size calibrationsystem and convert original defect size data to approximate actualdefect size data. The setup of the defect size calibration system isshown in FIG. 8A and FIG. 8B. It is shown in step 540. For every defectimage 1101, data processing center 21 collects the original defect sizebefore defect size calibration and real defect size after defect sizecalibration and performs statistical analysis to build up a statisticalmodel. The defect size X-axis and Y-axis data for defect image 1101 willbe corrected based on this statistical model. For example: Use thedefect image 1101 with KDI value 1 to do defect size calibration. Thestatistical model for the defect size calibration is built up. A defectsize calibration factor is defined to be equal to 0.85. For example: InFIG. 8C, the original defect size for 4th defect image 1101 is 150 nm inX-axis and 150 nm in Y-axis. Then, do defect size calibration in step540. Need to multiply defect size calibration factor 0.85 with originaldefect size in defect image 1101 or polygon defect image 1103. Then,defect size after defect size calibration will be 130 nm in X-axis and130 nm in Y-axis for defect image 1104 or polygon defect image 1105after calibration. Or use the defect image 1101 with KDI value 0.5 to dodefect size calibration. The statistical model for the defect sizecalibration is built up. A defect size calibration factor is defined tobe equal to 0.9. The defect size conversion process is same as describedabove. It is not repeated again. Then, it is shown as in step 550. Dataprocessing center 21 extracts the calibration factor from defect sizecalibration statistical model, executes defect size calibrationautomatically for every defect image 1101, and overlaps onto the mappeddesign layout pattern 1110 relative to defect image 1101 coordinate.Finally, data processing center 21 or engineer can get more accurateKiller Defect Index for every defect image 1101 with new calibration.

After the calibration procedure 8A, 8B, and 8C, user can get moreprecise real defect size data. Since the killer defect index isproportional to the defect size. Incorrect defect size would causedefect count of higher killer defect index higher than it should be.That makes the selection of short failure defect or open failure defecteven harder. Eventually, that increase time to improve wafer yield andcost. In FIG. 8C, the table 1160 shows the defect size data before andafter defect size calibration. Obviously, the best result will be thecalibration with SEM defect inspection data. Next will be to calibratewith a defect size calibration factor. When the calibration sample countis higher in determining the defect size calibration factor, thestatistical defect size calibration factor will be closer to the SEMdefect inspection data.

Please see FIG. 2 . After processing defect open circuit failure orshort circuit failure type analysis in step 150 and step 160 andcalibration in step 500, the present invention can further performdefect classification for defect image 1101. As shown in step 170, thedefect can be classified as Non-killer defect or Killer defect based onthe defect calibration result in previous step. For example: The resultby implementing step 150 is to classify defect based on KDI value,defect signal parameters, and whether a defect matches defect pattern indefect pattern library database and high frequent failure defectlibrary. Defect signal parameters is the intensity or brightness valueof every pixel data in two-dimensional vector analysis obtained fromimage processing analysis of the defect image 1101 of image file 1001.It is shown in FIG. 11A. The horizontal axis is the intensity. Thevertical axis is in pixel count unit. Also, contrast value is therelative intensity ratio between defect pattern and its surroundingbackground pattern using image processing analysis method. Polarityvalue is to imply a relative position between the defect pattern andbackground pattern by checking the defect pattern and its light shadowchange using image processing analysis method. In step 160, it is tooverlap defect contour with two different polygon patterns or onepolygon pattern and check whether this defect contour is classified as anon-killer defect, or open circuit failure type killer defect, or shortcircuit failure type killer defect. Detail execution procedure will bedescribed in later section.

In addition, process step 180 in FIG. 2 to setup defect pattern libraryand frequent failure defect library. The defect pattern library sourceis described below. One defect pattern is layout pattern with designrule check error. For example: Spacing distance rule is 30 nm. Actualdrawing layout pattern is 28 nm. This will narrow process window andresult in lower wafer yield. So, it will be selected into defect patternlibrary as shown in FIG. 11B. Another defect pattern source is from theDesign for Manufacturing (DFM) simulation analysis of a design layout1110. If there is layout pattern showing high risk pattern from the DFMsimulation analysis. Since it could narrow process window as well, it ispossible to cause lower wafer yield. Further defect inspection of thislayout pattern on wafer process window check is needed to verify ifthere is open circuit failure defect or short circuit failure defectbased on the layout pattern locations on wafer. That is why it isselected into defect pattern library as shown in FIG. 11C X marker. Theway to setup frequent failure defect library is to combine plurality ofactual defect image 1101 data from optical inspection tool and performpattern match method to obtain pattern group with identical or similarpattern as shown in FIG. 11D. When the circuit layout pattern is densein a design layout, it belongs to a frequent failure defect pattern.Further defect inspection of this layout pattern on wafer process windowcheck is needed to verify if there is open circuit failure defect orshort circuit failure defect based on the frequent failure defect layoutpattern locations on wafer. That is why it is selected into frequentfailure defect library. User can collect systematic defect layoutpattern 1111, open circuit failure type or short circuit failure typedefect layout pattern found in failure analysis, layout pattern with DRCerror, and Design for Manufacturing check as weak pattern together andsetup into defect pattern library. User can also reference frequentfailure defect library patent method cited in US patent no. U.S. Pat.No. 8,607,169B2 of the same inventor and setup frequent failure defectlibrary. In step 170, the system will execute pattern match (patternmatch patent method reference is cited in ROC Taiwan patent no. 15346462of the same inventor) between the defect layout pattern from defectinspection data of a defect inspection tool and those defect layoutpattern in defect pattern library and frequent failure defect library.It is to find if there is identical or similar defect layout pattern forfurther defect analysis.

Besides, please refer to FIG. 9 , FIG. 9 shows a flowchart to executethe pattern match analysis between defect contour, defect image, anddesign layout pattern polygon. As shown in FIG. 9 , step 160 isperformed to execute the pattern match analysis between defect contour,defect image, and design layout pattern polygon and judge whether thereis a short failure defect or open failure defect. Then, defect isclassified. As shown in step 1610: If there is either no circuit pattern1113 or dummy pattern inside the coordinate deviation region where thecenter of coordinate deviation region is the defect image 1101 mapped atthe defect layout pattern 1111. Since there is no short failure or openfailure possibility, the judgment is a dummy pattern defect. Dummypattern defect, as shown in FIG. 11E, is categorized as a non-killerdefect. Next, it is shown in step 1620. If there is circuit pattern 1113inside the coordinate deviation region where the center of coordinatedeviation region is the defect image 1101 mapped at the defect layoutpattern 1111. From the pattern match analysis between defect contour,defect image, and design layout pattern polygon of step 160, there is noshort failure or open failure possibility. It is judged to be a nuisancedefect. As shown in FIG. 11F, only a circuit layout pattern 1113 locatedat one side of the defect layout pattern 1111. The defect will not causeshort failure or open failure in FIG. 11F. Nuisance defect, as shown inFIG. 11F, is categorized as a non-killer defect. Furthermore, it isshown in step 1630. If there is circuit pattern 1113 inside thecoordinate deviation region where the center of coordinate deviationregion is the defect image 1101 mapped at the defect layout pattern1111. From the pattern match analysis between defect contour and designlayout pattern polygon of step 160, there is short failure or openfailure possibility. It is judged to be a short defect or open defect.As shown in FIG. 7D, short defect or open defect is categorized as akiller defect.

Please refer to FIG. 10 . It is a defect classification flowchart of thepresent invention. Obtain defect signal parameter data and KDI data asshown in FIG. 10 . Based on KDI value and defect signal parameter valueof each defect, and whether the defect matches defect pattern in defectpattern library and frequent failure defect library, the defect isclassified as Non-killer defect and Killer defect. It is a criterionused for defect sample selection. In step 1710, it is to retrieve defectdata and the defect signal parameter data after image processinganalysis. In step 1720, it is to retrieve KDI data that is from CriticalArea Analysis method in step 150 process. In step 1730, it is toclassify defect based on KDI value and defect signal parameter value ofeach defect, and whether the defect matches defect pattern in defectpattern library and frequent failure defect library. Please referencedefect pattern library from FIG. 11A to 11G. In step 1740, it is tojudge whether there is possibility of either open circuit type failureor short circuit type failure. For example: When KDI value of a defectis equal to 0. No matter how much the defect signal parameter value (asshown in FIG. 11A) is, it is judged as a dummy pattern defect (as shownin FIG. 11E). It is classified as Non-killer defect. The defect will befiltered. That means the Non-killer defect is not considered in thedefect sample selection analysis. For example: There are 5000 defects indefect image file 1101. 3000 defects are Non-killer defect. Duringdefect sample selection analysis, those 3000 Non-killer defects are notconsidered for defect sample selection. In step 1750, it is to judge aKDI value to be equal to 0 or close to 0. No matter how much the defectsignal parameter value, it is judged as a Nuisance defect (as shown inFIG. 11F). It is classified as Non-killer defect. The defect will befiltered as well. The filter procedure is same as in step 1740. It isnot repeated here.

Next, implement step 1760. First, it is to do defect classification forthose defects not filtered from previous step. For example: Select highKDI value defect (For example: KDI value 0.75˜1) and high defect signalparameter value as first priority defect sample selection group. Afterthat, select high KDI value defect (For example: KDI value 0.75˜1) andmedium defect signal parameter value as second priority defect sampleselection group. The next is to select medium KDI value defect (Forexample: KDI value 0.5˜0.75) and high defect signal parameter value asthird priority defect sample selection group. Next, select medium KDIvalue defect (For example: KDI value 0.5˜0.75) and medium defect signalparameter value as fourth priority defect sample selection group. To bementioned here, all the above groups belong to high-risk killer defectsample selection group. It is under high priority to monitor if anyfurther process, equipment problem must be fixed, or layout pattern mustbe modified. If there is time limitation, user can select the firstpriority defect sample selection group to inspect the defect patternclosely. How to decide the defect sample selection group, the presentinvention does not put any limitation for defect sample selection.

Even more, select low KDI value defect (For example: KDI value 0.2˜0.5)and high, medium defect signal parameter value as fifth or leastpriority defect sample selection group. Since low KDI value defect is alow-risk killer defect, it is only selected at minimum defect samplequantity or even selected occasionally. As to lowest KDI group defect(For example: KDI<0.2 and KDI>0), it is a negligible risk killer defect.Since negligible risk killer defect approximate nuisance defect, it isclose to Non-killer defect. So, there is no need to do defect sampleselection for negligible risk killer defect. The negligible risk killerdefect will be filtered as well. Those procedures mentioned abovealready implements defect classification and defect sample selectiongroup for all the defect image 1101 on wafer 10. So, those defectclassification and defect sample selection result will be recorded intodefect text and image data file 1130 with more result data added (asshown in FIG. 3B, more column of defect information is added). Eachdefect image 1101 is marked what defect classification group and defectsample selection priority group the defect belongs to. Of course, theadded defect information in the defect text and image file 1130 will beupdated and stored into memory unit 31.

Besides, it is shown in step 1770. After finishing defect classificationin step 1730 and perform pattern match with defect pattern library andfrequent failure defect library. If there is identical or similar defectpattern and the defect pattern is a alarm defect pattern, then thedefect must be sampled at defect sample selection procedure. On theother hand, the defect will be filtered if the defect pattern is a falsedefect layout pattern (as shown in FIG. 11G).

Smart defect calibration system and the method thereof of the presentinvention utilize defect coordinate calibration system and defect sizecalibration system to improve the Critical Area Analysis precision levelfor defect analysis. That raises the killer defect judgment precisionlevel when open circuit failure or short circuit failure probability ofsemiconductor defect can be judged correctly. The non-killer defect willbe filtered and excluded in defect sample selection procedure. CombineKDI data and defect signal parameter data to setup killer defect and itsrisk classification level as defect sample selection priority sequence.Comparing to traditional defect sample selection standard based ondefect signal intensity and defect size dimension criteria, the presentinvention increases the discovery of real open circuit failure killerdefect or short circuit failure killer defect in much better capability.It will shorten defect yield learning curve time and raise to higherwafer yield as well. Ramp production at earlier time will increaserevenue. Wafer yield increase reduces cost.

Although specific embodiments have been illustrated and described, itwill be appreciated by those skilled in the art that variousmodifications may be made without departing from the scope of thepresent invention, which is intended to be limited solely by theappended claims.

What is claimed is:
 1. A smart coordinate conversion and calibrationsystem in semiconductor wafer manufacturing, including a storageapparatus, a wafer manufacturing set having a wafer manufacturingequipment, a wafer defect inspection tool and a data processingapparatus, the storage apparatus is used to store an integrated circuitdesign layout file including a plurality of circuit layout patterns, thewafer manufacturing set arranges the circuit layout patterns of theintegrated circuit design layout files onto wafer, the wafer defectinspection tool scans the wafer to retrieve a defect scan and inspectiondata, the data processing apparatus converts the defect scan andinspection data into a defect text and image data file including aplurality of defect data on the wafer, and each of the defect data atleast includes a defect coordinate, a defect size, a defect area and adefect intensity value of a defect contour, wherein: the data processingapparatus adjusts dimension units of the defect size, a defectcoordinate and the circuit layout pattern to be the same dimension unit;the data processing apparatus converts the defect contour onto a correctdefect coordinate (X₂, Y₂) of the circuit layout pattern for a real-timepattern match; the data processing apparatus executes a coordinatedeviation calibration, which includes: a user performs a manual patternmatch to measure a coordinate deviation calibration value (X₂′-X₂,Y₂′-Y₂), by adjusting the dimension unit of the displayed defect layoutpattern and the defect contour to be the same, and manually marking adefect contour location to a corresponding location in the circuitlayout pattern as a new coordinate location (X₂′, Y₂′), wherein if thecorrect defect coordinate (X₂, Y₂) in a converted defect layout patternis not in the same location with the new coordinate location (X₂′, Y₂′)in the defect contour, the coordinate deviation calibration must beperformed to calibrate the new coordinate location (X₂′, Y₂′) and thecoordinate deviation calibration is accomplished via a mouse cursormanually marking the defect coordinate corresponding to the location inthe circuit layout pattern as the new coordinate location (X₂′, Y₂′);the data processing apparatus obtains the coordinate deviationcalibration value (X₂′-X₂, Y₂′-Y₂), the coordinate deviation calibrationvalue (X₂′-X₂, Y₂′-Y₂) includes an average coordinate precision valueand a coordinate precision standard deviation value for X-axis andY-axis, the average coordinate precision value and the coordinateprecision standard deviation value are calculated via a statisticalanalysis; and the data processing apparatus introduces the averagecoordinate precision value and the coordinate precision standarddeviation value into the smart coordinate conversion and calibrationsystem in order to overlap or map a defect coordinate (X₁, Y₁) to thenew coordinate location (X₂′, Y₂′) based on the coordinate deviationcalibration value (X₂′-X₂, Y₂′-Y₂) after the coordinate deviationcalibration, wherein after the average coordinate precision value andthe coordinate precision standard deviation value are introduced intothe smart coordinate conversion and calibration system, the dataprocessing apparatus executes an overlapping procedure, and retrievessequentially the defect coordinate, the defect size and the defect areaof the defect contour from the defect text and image data file, andoverlaps the defect size and the defect area onto the coordinatedeviation calibration value (X₂′-X₂, Y₂′-Y₂) at a coordinate deviationarea of the circuit layout pattern based on the corresponding defectcoordinate after the conversion and calibration of coordinate.
 2. Thesmart coordinate conversion and calibration system of claim 1, whereinthe manual pattern match includes manually processing a coordinatedeviation distance from a defect layout pattern coordinate to an actualdefect layout pattern coordinate to map the defect contour on a monitorscreen, then the defect layout pattern and the defect contour arealigned manually with a setting coordinate value.
 3. The smartcoordinate conversion and calibration system of claim 1, wherein afterthe overlapping procedure, the data processing apparatus executes aCritical Area Analysis (CAA), based on the overlapping of the calibrateddefect size and defect area onto the mapped circuit layout pattern, anduses the CAA to analyze a critical area within the coordinate deviationarea for each of the defects, and decides a Killer Defect Index (KDI)value.
 4. The smart coordinate conversion and calibration system ofclaim 3, wherein after the CAA, the data processing apparatus classifieseach of the defects based on the KDI value of each defect and the defectintensity value of the defect contour, and the data processing apparatusexecutes a defect sample selection based on defect classification resultin order to detect whether cause an open circuit failure or a shortcircuit failure or not.
 5. A smart coordinate conversion and calibrationsystem in semiconductor wafer manufacturing, including a storageapparatus, a wafer manufacturing set having a wafer manufacturingequipment, a wafer defect inspection tool and a data processingapparatus, the storage apparatus is used to store an integrated circuitdesign layout file including a plurality of circuit layout patterns, thewafer manufacturing set arranges the circuit layout patterns of theintegrated circuit design layout files onto wafer, the wafer defectinspection tool scans the wafer to retrieve a defect scan and inspectiondata, the data processing apparatus converts the defect scan andinspection data into a defect text and image data file including aplurality of defect data on the wafer, and each of the defect data atleast includes a defect coordinate, a defect size, a defect area and adefect intensity value of a defect contour, wherein: the data processingapparatus adjusts dimension units of the defect size, a defectcoordinate and the circuit layout pattern to be the same dimension unit;the data processing apparatus converts the defect contour onto a correctdefect coordinate (X₂, Y₂) of the circuit layout pattern for a real-timepattern match; the data processing apparatus executes a coordinatedeviation calibration, which includes: a user performs a pattern matchusing a Graphical User Interface to measure a coordinate deviationcalibration value (X₂′-X₂, Y₂′-Y₂), and to mark a defect contourlocation to a corresponding location in the circuit layout pattern as anew coordinate location (X₂′, Y₂′); the data processing apparatusobtains the coordinate deviation calibration value (X₂′-X₂, Y₂′-Y₂), thecoordinate deviation calibration value (X₂′-X₂, Y₂′-Y₂) includes anaverage coordinate precision value and a coordinate precision standarddeviation value for X-axis and Y-axis, the average coordinate precisionvalue and the coordinate precision standard deviation value arecalculated via a statistical analysis; and the data processing apparatusintroduces the average coordinate precision value and the coordinateprecision standard deviation value into the smart coordinate conversionand calibration system in order to overlap or map a defect coordinate(X₁, Y₁) to the new coordinate location (X₂′, Y₂′) based on thecoordinate deviation calibration value (X₂′-X₂, Y₂′-Y₂) after thecoordinate deviation calibration, wherein after introducing the averagecoordinate precision value and the coordinate precision standarddeviation value, the data processing apparatus executes an overlappingprocedure, and retrieves sequentially the defect coordinate, the defectsize and the defect area of the defect contour from the defect text andimage data file, and overlaps the defect size and the defect area ontothe coordinate deviation calibration value (X₂′-X₂, Y₂′-Y₂) at acoordinate deviation area of the circuit layout pattern based on thecorresponding defect coordinate after the conversion and calibration ofcoordinate.
 6. The smart coordinate conversion and calibration system ofclaim 5, wherein the pattern match using a Graphical User Interfaceincludes that the user moves a mouse cursor to mark the defect contourlocation at the defect text and image data file mapped to thecorresponding location in the circuit layout pattern as the newcoordinate location (X₂′, Y₂′), then the defect coordinate (X₁, Y₁) isconverted to the correct defect coordinate (X₂, Y₂).
 7. The smartcoordinate conversion and calibration system of claim 5, wherein if thecorrect defect coordinate (X₂, Y₂) in a converted defect layout patternis not at the same location with the new coordinate location (X₂′, Y₂′)in the defect contour, the coordinate deviation calibration must beperformed to obtain the coordinate deviation calibration value (X₂′-X₂,Y₂′-Y₂).
 8. The smart coordinate conversion and calibration system ofclaim 5, wherein after the overlapping procedure, the data processingapparatus executes a Critical Area Analysis (CAA) based on theoverlapping of the calibrated defect size and defect area onto themapped circuit layout pattern, and uses the CAA to analyze a criticalarea within the coordinate deviation area for each of the defects, anddecides a Killer Defect Index (KDI) value.
 9. The smart coordinateconversion and calibration system of claim 8, wherein after the CAA, thedata processing apparatus classifies each of the defects based on theKDI value of each defect and the defect intensity value of the defectcontour, and executes a defect sample selection based on defectclassification result in order to detect whether cause an open circuitfailure or a short circuit failure or not.
 10. A smart coordinateconversion and calibration system in semiconductor wafer manufacturing,including a storage apparatus, a wafer manufacturing set having a wafermanufacturing equipment, a wafer defect inspection tool and a dataprocessing apparatus, the storage apparatus is used to store anintegrated circuit design layout file including a plurality of circuitlayout patterns, the wafer manufacturing set arranges the circuit layoutpatterns of the integrated circuit design layout files onto wafer, thewafer defect inspection tool scans the wafer to retrieve a defect scanand inspection data, the data processing apparatus converts the defectscan and inspection data into a defect text and image data fileincluding a plurality of defect data on the wafer, and each of thedefect data at least includes a defect coordinate, a defect size, adefect area and a defect intensity value of a defect contour, wherein:the data processing apparatus adjusts dimension units of the defectsize, a defect coordinate and the circuit layout pattern to be the samedimension unit; the data processing apparatus converts the defectcontour onto a correct defect coordinate (X₂, Y₂) of the circuit layoutpattern for a real-time pattern match; the data processing apparatusexecutes a coordinate deviation calibration, which includes: the dataprocessing apparatus performing an auto pattern match between the defectcontour in the circuit layout pattern and the defect contour in thedefect text and image data file to measure a coordinate deviationcalibration value (X₂′-X₂, Y₂′-Y₂), and to mark a defect contourlocation to a corresponding location in the circuit layout pattern as anew coordinate location (X₂′, Y₂′), wherein the auto pattern matchincludes that the defect contour location at the defect text and imagedata file mapped to the corresponding location in the circuit layoutpattern is auto-marked as the new coordinate location (X₂′, Y₂′), thenthe defect coordinate (X₁, Y₁) is converted to the correct defectcoordinate (X₂, Y₂; the data processing apparatus obtains the coordinatedeviation calibration value (X₂′-X₂, Y₂′-Y₂), the coordinate deviationcalibration value (X₂′-X₂, Y₂′-Y₂) includes an average coordinateprecision value and a coordinate precision standard deviation value forX-axis and Y-axis, the average coordinate precision value and thecoordinate precision standard deviation value are calculated via astatistical analysis; and the data processing apparatus introduces theaverage coordinate precision value and the coordinate precision standarddeviation value into the smart coordinate conversion and calibrationsystem in order to overlap or map a defect coordinate (X₁, Y₁) to thenew coordinate location (X₂′, Y₂′) based on the coordinate deviationcalibration value (X₂′-X₂, Y₂′-Y₂) after the coordinate deviationcalibration, wherein after introducing the average coordinate precisionvalue and the coordinate precision standard deviation value, the dataprocessing apparatus executes an overlapping procedure, and retrievessequentially the defect coordinate, the defect size and the defect areaof the defect contour from the defect text and image data file, andoverlaps the defect size and the defect area onto the coordinatedeviation calibration value (X₂′-X₂, Y₂′-Y₂) at a coordinate deviationarea of the circuit layout pattern based on the corresponding defectcoordinate after the conversion and calibration of coordinate.
 11. Thesmart coordinate conversion and calibration system of claim 10, whereinif the correct defect coordinate (X₂, Y₂) in a converted defect layoutpattern is not at the same location with the new coordinate location(X₂′, Y₂′) in the defect contour, the coordinate deviation calibrationmust be performed to obtain the coordinate deviation calibration value(X₂′-X₂, Y₂′-Y₂).
 12. The smart coordinate conversion and calibrationsystem of claim 10, wherein after the overlapping procedure, the dataprocessing apparatus executes a Critical Area Analysis (CAA) based onthe overlapping of the calibrated defect size and defect area onto themapped circuit layout pattern, and uses the CAA to analyze a criticalarea within the coordinate deviation area for each of the defects, anddecides a Killer Defect Index (KDI) value.
 13. The smart coordinateconversion and calibration system of claim 12, wherein after the CAA,the data processing apparatus classifies each of the defects based onthe KDI value of each defect and the defect intensity value of thedefect contour, and executes a defect sample selection based on defectclassification result in order to detect whether cause an open circuitfailure or a short circuit failure or not.